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@@ -5,7 +5,6 @@ language:
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  library_name: transformers
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  tags:
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  - linformer
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- - legal
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  - medical
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  - RoBERTa
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  - pytorch
@@ -29,7 +28,7 @@ Jargon is available in several versions with different context sizes and types o
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  | jargon-general-legal | jargon-general-base |18GB Legal Corpus
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  | [jargon-multidomain-base](https://huggingface.co/PantagrueLLM/jargon-multidomain-base) | jargon-general-base |Medical+Legal Corpora|
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  | jargon-legal | scratch |18GB Legal Corpus|
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- | jargon-legal-4096 | scratch |18GB Legal Corpus|
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  | [jargon-biomed](https://huggingface.co/PantagrueLLM/jargon-biomed) | scratch |5.4GB Medical Corpus|
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  | [jargon-biomed-4096](https://huggingface.co/PantagrueLLM/jargon-biomed-4096) | scratch |5.4GB Medical Corpus|
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  | [jargon-NACHOS](https://huggingface.co/PantagrueLLM/jargon-NACHOS) | scratch |[NACHOS](https://drbert.univ-avignon.fr/)|
@@ -45,7 +44,7 @@ For more info please check out the [paper](https://hal.science/hal-04535557/file
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  ## Using Jargon models with HuggingFace transformers
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- You can get started with `jargon-general-biomed` using the code snippet below:
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  ```python
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  from transformers import AutoModelForMaskedLM, AutoTokenizer, pipeline
 
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  library_name: transformers
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  tags:
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  - linformer
 
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  - medical
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  - RoBERTa
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  - pytorch
 
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  | jargon-general-legal | jargon-general-base |18GB Legal Corpus
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  | [jargon-multidomain-base](https://huggingface.co/PantagrueLLM/jargon-multidomain-base) | jargon-general-base |Medical+Legal Corpora|
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  | jargon-legal | scratch |18GB Legal Corpus|
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+ | [jargon-legal-4096](https://huggingface.co/PantagrueLLM/jargon-legal-4096) | scratch |18GB Legal Corpus|
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  | [jargon-biomed](https://huggingface.co/PantagrueLLM/jargon-biomed) | scratch |5.4GB Medical Corpus|
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  | [jargon-biomed-4096](https://huggingface.co/PantagrueLLM/jargon-biomed-4096) | scratch |5.4GB Medical Corpus|
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  | [jargon-NACHOS](https://huggingface.co/PantagrueLLM/jargon-NACHOS) | scratch |[NACHOS](https://drbert.univ-avignon.fr/)|
 
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  ## Using Jargon models with HuggingFace transformers
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+ You can get started with this model using the code snippet below:
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  ```python
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  from transformers import AutoModelForMaskedLM, AutoTokenizer, pipeline